Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2024.048112
Journal Menu
Special lssues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

An Intelligent Framework for Resilience Recovery of FANETs with Spatio-Temporal Aggregation and Multi-Head Attention Mechanism

Zhijun Guo1, Yun Sun2,*, Ying Wang1, Chaoqi Fu3, Jilong Zhong4,*
1 Air Traffic Control and Navigation College, Air Force Engineering University, Xi’an, 710000, China
2 Institute of Systems Engineering, Academy of Military Science, Beijing, 100101, China
3 Equipment Management and UAV Engineering College, Air Force Engineering University, Xi’an, 710038, China
4 Defense Innovation Institute, Academy of Military Science, Beijing, 100071, China
* Corresponding Author: Yun Sun. Email: email; Jilong Zhong. Email: email
(This article belongs to the Special Issue: Industrial Big Data and Artificial Intelligence-Driven Intelligent Perception, Maintenance, and Decision Optimization in Industrial Systems)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.048112

Received 28 November 2023; Accepted 22 March 2024; Published online 18 April 2024

Abstract

Due to the time-varying topology and possible disturbances in a conflict environment, it is still challenging to maintain the mission performance of flying Ad hoc networks (FANET), which limits the application of Unmanned Aerial Vehicle (UAV) swarms in harsh environments. This paper proposes an intelligent framework to quickly recover the cooperative coverage mission by aggregating the historical spatio-temporal network with the attention mechanism. The mission resilience metric is introduced in conjunction with connectivity and coverage status information to simplify the optimization model. A spatio-temporal node pooling method is proposed to ensure all node location features can be updated after destruction by capturing the temporal network structure. Combined with the corresponding Laplacian matrix as the hyperparameter, a recovery algorithm based on the multi-head attention graph network is designed to achieve rapid recovery. Simulation results showed that the proposed framework can facilitate rapid recovery of the connectivity and coverage more effectively compared to the existing studies. The results demonstrate that the average connectivity and coverage results is improved by 17.92% and 16.96%, respectively compared with the state-of-the-art model. Furthermore, by the ablation study, the contributions of each different improvement are compared. The proposed model can be used to support resilient network design for real-time mission execution.

Keywords

Resilience; cooperative mission; FANET; spatio-temporal node pooling; multi-head attention graph network

  • 117

    View

  • 20

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link